Abstract：
Nowadays, many e-commerce websites allow users to login with their existing social networking accounts. When a new user comes to an e-commerce website, it is interesting to study whether the information from external social media platforms can be utilized to alleviate the cold-start problem. In this paper, we focus on a specific task on cross-site information sharing, i.e., leveraging the text posted by a user on the social media platform (termed as social text) to infer his/her purchase preference of product categories on an e-commerce platform. To solve the task, a key problem is how to effectively represent the social text in a way that its information can be utilized on the e-commerce platform. We study two major kinds of text representation methods for predicting cross-site purchase preference, including shallow textual features and deep textual features learned by deep neural network models. We conduct extensive experiments on a large linked dataset, and our experimental results indicate that it is promising to utilize the social text for predicting purchase preference. Specially, the deep neural network approach has shown a more powerful predictive ability when the number of categories becomes large.

This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61502502 and 61502501, the National Basic Research 973 Program of China under Grant No. 2014CB340403 and the Beijing Natural Science Foundation under Grant No. 4162032. Ting Bai was supported by the Outstanding Innovative Talents Cultivation Funded Programs 2016 of Renmin Univertity of China. Wayne Xin Zhao was also supported by the Opening Project of Guangdong Province Key Laboratory of Big Data Analysis and Processing under Grant No. 2017001.

通讯作者: Wayne-Xin Zhao
Email: batmanfly@gmail.com

About author: Ting Bai is currently a Ph.D. student at the School of Information, Renmin University of China, Beijing. She is working in Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing. Her research mainly focuses on social content analysis, recommender systems and deep learning, especially on commercial intent detection and human behavior analysis.